Overview

Dataset statistics

Number of variables18
Number of observations8760
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory123.0 B

Variable types

Numeric9
Categorical6
Boolean3

Alerts

date has a high cardinality: 365 distinct values High cardinality
month is highly correlated with yearHigh correlation
year is highly correlated with monthHigh correlation
dayofweek_n is highly correlated with working_dayHigh correlation
working_day is highly correlated with dayofweek_nHigh correlation
month is highly correlated with yearHigh correlation
year is highly correlated with monthHigh correlation
dayofweek_n is highly correlated with working_dayHigh correlation
working_day is highly correlated with dayofweek_nHigh correlation
rain is highly correlated with year and 3 other fieldsHigh correlation
temp is highly correlated with year and 3 other fieldsHigh correlation
rhum is highly correlated with year and 3 other fieldsHigh correlation
wdsp is highly correlated with year and 3 other fieldsHigh correlation
hour is highly correlated with year and 3 other fieldsHigh correlation
day is highly correlated with year and 3 other fieldsHigh correlation
month is highly correlated with year and 3 other fieldsHigh correlation
year is highly correlated with rain and 9 other fieldsHigh correlation
holiday is highly correlated with rain and 9 other fieldsHigh correlation
dayofweek_n is highly correlated with working_day and 1 other fieldsHigh correlation
working_day is highly correlated with rain and 9 other fieldsHigh correlation
peak is highly correlated with rain and 9 other fieldsHigh correlation
working_day is highly correlated with dayofweekHigh correlation
year is highly correlated with seasonHigh correlation
season is highly correlated with yearHigh correlation
dayofweek is highly correlated with working_dayHigh correlation
rain is highly correlated with rain_typeHigh correlation
temp is highly correlated with month and 1 other fieldsHigh correlation
rhum is highly correlated with hourHigh correlation
hour is highly correlated with rhum and 3 other fieldsHigh correlation
month is highly correlated with temp and 2 other fieldsHigh correlation
year is highly correlated with month and 1 other fieldsHigh correlation
dayofweek_n is highly correlated with dayofweek and 1 other fieldsHigh correlation
dayofweek is highly correlated with dayofweek_n and 1 other fieldsHigh correlation
working_day is highly correlated with dayofweek_n and 2 other fieldsHigh correlation
season is highly correlated with temp and 2 other fieldsHigh correlation
peak is highly correlated with hour and 2 other fieldsHigh correlation
timesofday is highly correlated with hour and 2 other fieldsHigh correlation
rain_type is highly correlated with rainHigh correlation
count is highly correlated with hour and 1 other fieldsHigh correlation
date is uniformly distributed Uniform
rain has 7862 (89.7%) zeros Zeros
hour has 365 (4.2%) zeros Zeros
dayofweek_n has 1272 (14.5%) zeros Zeros
count has 1794 (20.5%) zeros Zeros

Reproduction

Analysis started2022-04-24 10:46:29.023785
Analysis finished2022-04-24 10:47:09.878372
Duration40.85 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

rain
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06842465753
Minimum0
Maximum10.3
Zeros7862
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2022-04-24T11:47:10.153872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3
Maximum10.3
Range10.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3533969449
Coefficient of variation (CV)5.164760155
Kurtosis155.9776871
Mean0.06842465753
Median Absolute Deviation (MAD)0
Skewness10.03913757
Sum599.4
Variance0.1248894006
MonotonicityNot monotonic
2022-04-24T11:47:10.543034image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
07862
89.7%
0.1272
 
3.1%
0.2112
 
1.3%
0.381
 
0.9%
0.461
 
0.7%
0.650
 
0.6%
0.539
 
0.4%
0.736
 
0.4%
0.927
 
0.3%
0.826
 
0.3%
Other values (38)194
 
2.2%
ValueCountFrequency (%)
07862
89.7%
0.1272
 
3.1%
0.2112
 
1.3%
0.381
 
0.9%
0.461
 
0.7%
0.539
 
0.4%
0.650
 
0.6%
0.736
 
0.4%
0.826
 
0.3%
0.927
 
0.3%
ValueCountFrequency (%)
10.31
< 0.1%
6.11
< 0.1%
5.52
< 0.1%
5.21
< 0.1%
5.12
< 0.1%
4.91
< 0.1%
4.71
< 0.1%
4.61
< 0.1%
4.51
< 0.1%
4.41
< 0.1%

temp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct293
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.18797945
Minimum-4.5
Maximum26.3
Zeros12
Zeros (%)0.1%
Negative137
Negative (%)1.6%
Memory size68.6 KiB
2022-04-24T11:47:10.917582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4.5
5-th percentile2
Q16.6
median10
Q313.9
95-th percentile18.3
Maximum26.3
Range30.8
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation5.036550324
Coefficient of variation (CV)0.4943620418
Kurtosis-0.3417585134
Mean10.18797945
Median Absolute Deviation (MAD)3.6
Skewness0.0770660156
Sum89246.7
Variance25.36683916
MonotonicityNot monotonic
2022-04-24T11:47:12.286719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.683
 
0.9%
9.283
 
0.9%
8.781
 
0.9%
881
 
0.9%
8.980
 
0.9%
10.180
 
0.9%
13.279
 
0.9%
10.779
 
0.9%
7.878
 
0.9%
9.377
 
0.9%
Other values (283)7959
90.9%
ValueCountFrequency (%)
-4.51
 
< 0.1%
-41
 
< 0.1%
-3.91
 
< 0.1%
-3.42
< 0.1%
-3.31
 
< 0.1%
-3.22
< 0.1%
-31
 
< 0.1%
-2.93
< 0.1%
-2.81
 
< 0.1%
-2.61
 
< 0.1%
ValueCountFrequency (%)
26.33
< 0.1%
26.21
 
< 0.1%
25.91
 
< 0.1%
25.72
< 0.1%
25.61
 
< 0.1%
25.43
< 0.1%
25.32
< 0.1%
25.21
 
< 0.1%
25.12
< 0.1%
251
 
< 0.1%

rhum
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct69
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.33047945
Minimum24
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2022-04-24T11:47:13.595012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile59
Q175
median84
Q391
95-th percentile98
Maximum100
Range76
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.67070283
Coefficient of variation (CV)0.1417543406
Kurtosis0.4806764447
Mean82.33047945
Median Absolute Deviation (MAD)8
Skewness-0.8465907315
Sum721215
Variance136.2053045
MonotonicityNot monotonic
2022-04-24T11:47:13.960016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89348
 
4.0%
88333
 
3.8%
90328
 
3.7%
91327
 
3.7%
87319
 
3.6%
94304
 
3.5%
95302
 
3.4%
82300
 
3.4%
92296
 
3.4%
84295
 
3.4%
Other values (59)5608
64.0%
ValueCountFrequency (%)
241
 
< 0.1%
311
 
< 0.1%
321
 
< 0.1%
331
 
< 0.1%
361
 
< 0.1%
371
 
< 0.1%
381
 
< 0.1%
392
< 0.1%
404
< 0.1%
413
< 0.1%
ValueCountFrequency (%)
100171
2.0%
99116
 
1.3%
98167
1.9%
97203
2.3%
96248
2.8%
95302
3.4%
94304
3.5%
93295
3.4%
92296
3.4%
91327
3.7%

wdsp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.635502283
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2022-04-24T11:47:14.192747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median8
Q311
95-th percentile17
Maximum35
Range34
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.445983842
Coefficient of variation (CV)0.5148494779
Kurtosis1.55646894
Mean8.635502283
Median Absolute Deviation (MAD)3
Skewness1.007973409
Sum75647
Variance19.76677232
MonotonicityNot monotonic
2022-04-24T11:47:14.403197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
6911
10.4%
7893
10.2%
5801
9.1%
8789
9.0%
9694
 
7.9%
4677
 
7.7%
10650
 
7.4%
11556
 
6.3%
3486
 
5.5%
12429
 
4.9%
Other values (23)1874
21.4%
ValueCountFrequency (%)
157
 
0.7%
2257
 
2.9%
3486
5.5%
4677
7.7%
5801
9.1%
6911
10.4%
7893
10.2%
8789
9.0%
9694
7.9%
10650
7.4%
ValueCountFrequency (%)
352
 
< 0.1%
331
 
< 0.1%
311
 
< 0.1%
306
0.1%
294
 
< 0.1%
287
0.1%
276
0.1%
264
 
< 0.1%
258
0.1%
2410
0.1%

date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct365
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
2021-03-01
 
24
2021-11-07
 
24
2021-11-05
 
24
2021-11-04
 
24
2021-11-03
 
24
Other values (360)
8640 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01
2nd row2021-03-01
3rd row2021-03-01
4th row2021-03-01
5th row2021-03-01

Common Values

ValueCountFrequency (%)
2021-03-0124
 
0.3%
2021-11-0724
 
0.3%
2021-11-0524
 
0.3%
2021-11-0424
 
0.3%
2021-11-0324
 
0.3%
2021-11-0224
 
0.3%
2021-11-0124
 
0.3%
2021-10-3124
 
0.3%
2021-10-3024
 
0.3%
2021-10-2924
 
0.3%
Other values (355)8520
97.3%

Length

2022-04-24T11:47:14.614477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-03-0124
 
0.3%
2021-03-2424
 
0.3%
2021-03-0324
 
0.3%
2021-03-0424
 
0.3%
2021-03-0524
 
0.3%
2021-03-0624
 
0.3%
2021-03-0724
 
0.3%
2021-03-0824
 
0.3%
2021-03-0924
 
0.3%
2021-03-1024
 
0.3%
Other values (355)8520
97.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hour
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros365
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2022-04-24T11:47:14.797321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.922581688
Coefficient of variation (CV)0.6019636251
Kurtosis-1.204176265
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum100740
Variance47.92213723
MonotonicityNot monotonic
2022-04-24T11:47:15.063031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0365
 
4.2%
1365
 
4.2%
22365
 
4.2%
21365
 
4.2%
20365
 
4.2%
19365
 
4.2%
18365
 
4.2%
17365
 
4.2%
16365
 
4.2%
15365
 
4.2%
Other values (14)5110
58.3%
ValueCountFrequency (%)
0365
4.2%
1365
4.2%
2365
4.2%
3365
4.2%
4365
4.2%
5365
4.2%
6365
4.2%
7365
4.2%
8365
4.2%
9365
4.2%
ValueCountFrequency (%)
23365
4.2%
22365
4.2%
21365
4.2%
20365
4.2%
19365
4.2%
18365
4.2%
17365
4.2%
16365
4.2%
15365
4.2%
14365
4.2%

day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.72054795
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2022-04-24T11:47:15.350247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.796749115
Coefficient of variation (CV)0.5595701337
Kurtosis-1.193150834
Mean15.72054795
Median Absolute Deviation (MAD)8
Skewness0.007522437488
Sum137712
Variance77.382795
MonotonicityNot monotonic
2022-04-24T11:47:15.609984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1288
 
3.3%
2288
 
3.3%
28288
 
3.3%
27288
 
3.3%
26288
 
3.3%
25288
 
3.3%
24288
 
3.3%
23288
 
3.3%
22288
 
3.3%
21288
 
3.3%
Other values (21)5880
67.1%
ValueCountFrequency (%)
1288
3.3%
2288
3.3%
3288
3.3%
4288
3.3%
5288
3.3%
6288
3.3%
7288
3.3%
8288
3.3%
9288
3.3%
10288
3.3%
ValueCountFrequency (%)
31168
1.9%
30264
3.0%
29264
3.0%
28288
3.3%
27288
3.3%
26288
3.3%
25288
3.3%
24288
3.3%
23288
3.3%
22288
3.3%

month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.526027397
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2022-04-24T11:47:15.906913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.448048134
Coefficient of variation (CV)0.5283533035
Kurtosis-1.207055959
Mean6.526027397
Median Absolute Deviation (MAD)3
Skewness-0.01045819518
Sum57168
Variance11.88903593
MonotonicityNot monotonic
2022-04-24T11:47:16.111731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3744
8.5%
5744
8.5%
7744
8.5%
8744
8.5%
10744
8.5%
12744
8.5%
1744
8.5%
4720
8.2%
6720
8.2%
9720
8.2%
Other values (2)1392
15.9%
ValueCountFrequency (%)
1744
8.5%
2672
7.7%
3744
8.5%
4720
8.2%
5744
8.5%
6720
8.2%
7744
8.5%
8744
8.5%
9720
8.2%
10744
8.5%
ValueCountFrequency (%)
12744
8.5%
11720
8.2%
10744
8.5%
9720
8.2%
8744
8.5%
7744
8.5%
6720
8.2%
5744
8.5%
4720
8.2%
3744
8.5%

year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
2021
7344 
2022
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
20217344
83.8%
20221416
 
16.2%

Length

2022-04-24T11:47:16.352695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T11:47:16.491479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
20217344
83.8%
20221416
 
16.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

holiday
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
False
8568 
True
 
192
ValueCountFrequency (%)
False8568
97.8%
True192
 
2.2%
2022-04-24T11:47:16.570867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

dayofweek_n
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.991780822
Minimum0
Maximum6
Zeros1272
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2022-04-24T11:47:16.688025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.00351923
Coefficient of variation (CV)0.6696744679
Kurtosis-1.252932851
Mean2.991780822
Median Absolute Deviation (MAD)2
Skewness0.003108904696
Sum26208
Variance4.014089305
MonotonicityNot monotonic
2022-04-24T11:47:16.865509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
01272
14.5%
11248
14.2%
21248
14.2%
31248
14.2%
41248
14.2%
51248
14.2%
61248
14.2%
ValueCountFrequency (%)
01272
14.5%
11248
14.2%
21248
14.2%
31248
14.2%
41248
14.2%
51248
14.2%
61248
14.2%
ValueCountFrequency (%)
61248
14.2%
51248
14.2%
41248
14.2%
31248
14.2%
21248
14.2%
11248
14.2%
01272
14.5%

dayofweek
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Monday
1272 
Tuesday
1248 
Wednesday
1248 
Thursday
1248 
Friday
1248 
Other values (2)
2496 

Length

Max length9
Median length7
Mean length7.139726027
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonday
2nd rowMonday
3rd rowMonday
4th rowMonday
5th rowMonday

Common Values

ValueCountFrequency (%)
Monday1272
14.5%
Tuesday1248
14.2%
Wednesday1248
14.2%
Thursday1248
14.2%
Friday1248
14.2%
Saturday1248
14.2%
Sunday1248
14.2%

Length

2022-04-24T11:47:17.094469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T11:47:17.267519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
monday1272
14.5%
tuesday1248
14.2%
wednesday1248
14.2%
thursday1248
14.2%
friday1248
14.2%
saturday1248
14.2%
sunday1248
14.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

working_day
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
True
6120 
False
2640 
ValueCountFrequency (%)
True6120
69.9%
False2640
30.1%
2022-04-24T11:47:17.396075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

season
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Summer
2256 
Spring
2208 
Winter
2160 
Autumn
2136 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Summer2256
25.8%
Spring2208
25.2%
Winter2160
24.7%
Autumn2136
24.4%

Length

2022-04-24T11:47:17.508478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T11:47:17.633862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
summer2256
25.8%
spring2208
25.2%
winter2160
24.7%
autumn2136
24.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

peak
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
False
6210 
True
2550 
ValueCountFrequency (%)
False6210
70.9%
True2550
29.1%
2022-04-24T11:47:17.725444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

timesofday
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
Night
2920 
Afternoon
2190 
Morning
1825 
Evening
1825 

Length

Max length9
Median length7
Mean length6.833333333
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowNight
5th rowNight

Common Values

ValueCountFrequency (%)
Night2920
33.3%
Afternoon2190
25.0%
Morning1825
20.8%
Evening1825
20.8%

Length

2022-04-24T11:47:17.870179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T11:47:18.055480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
night2920
33.3%
afternoon2190
25.0%
morning1825
20.8%
evening1825
20.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rain_type
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
no rain
7862 
drizzle
 
465
moderate rain
 
320
light rain
 
100
heavy rain
 
13

Length

Max length13
Median length7
Mean length7.257876712
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno rain
2nd rowno rain
3rd rowno rain
4th rowno rain
5th rowno rain

Common Values

ValueCountFrequency (%)
no rain7862
89.7%
drizzle465
 
5.3%
moderate rain320
 
3.7%
light rain100
 
1.1%
heavy rain13
 
0.1%

Length

2022-04-24T11:47:18.191078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-24T11:47:18.337200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
rain8295
48.6%
no7862
46.1%
drizzle465
 
2.7%
moderate320
 
1.9%
light100
 
0.6%
heavy13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.780707763
Minimum0
Maximum26
Zeros1794
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2022-04-24T11:47:18.482066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile11
Maximum26
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.619783957
Coefficient of variation (CV)0.9574355344
Kurtosis1.395419616
Mean3.780707763
Median Absolute Deviation (MAD)2
Skewness1.167339767
Sum33119
Variance13.1028359
MonotonicityNot monotonic
2022-04-24T11:47:18.691835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
01794
20.5%
11229
14.0%
21000
11.4%
3898
10.3%
4775
8.8%
5645
 
7.4%
6597
 
6.8%
7471
 
5.4%
8368
 
4.2%
9274
 
3.1%
Other values (15)709
 
8.1%
ValueCountFrequency (%)
01794
20.5%
11229
14.0%
21000
11.4%
3898
10.3%
4775
8.8%
5645
 
7.4%
6597
 
6.8%
7471
 
5.4%
8368
 
4.2%
9274
 
3.1%
ValueCountFrequency (%)
261
 
< 0.1%
242
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
205
 
0.1%
198
 
0.1%
188
 
0.1%
1716
0.2%
1619
0.2%
1532
0.4%

Interactions

2022-04-24T11:47:06.993425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:50.867645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:52.682155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:54.222952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:56.340457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:58.735677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:00.751985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:02.389719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:04.304850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:07.182724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:51.068718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:52.846664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:54.416106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:56.512360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:59.001534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:00.920652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:02.545234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:04.469409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:07.367068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:51.258402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:53.027485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:54.590372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:56.686454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:59.185886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:01.120018image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:02.712967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:04.655957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:07.555626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:51.428765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:53.204451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:54.767114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:56.873292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:59.368365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:01.312954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:02.895355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:05.289653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:07.731181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:51.599855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:53.373715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:54.945818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:57.065890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:59.547306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:01.503519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:03.087079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:05.880951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:07.897595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:51.761306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:53.530452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:55.575198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:57.242429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:59.717483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:01.674685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:03.232587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:06.199584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:08.078963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:52.169263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:53.691101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:55.811891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:57.794091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:59.986937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:01.847551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:03.410139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:06.436268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:08.250989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:52.348255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:53.851533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:55.984035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:57.983245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:00.220718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:02.044173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:03.574003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:06.617099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:08.771766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:52.518277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:54.027603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:56.166830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:46:58.460402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:00.559099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:02.220441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:04.062117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-24T11:47:06.800687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-04-24T11:47:18.925080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-24T11:47:19.210293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-24T11:47:19.492378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-24T11:47:19.748373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-24T11:47:20.000755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-24T11:47:09.129812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-24T11:47:09.649951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

raintemprhumwdspdatehourdaymonthyearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayrain_typecount
00.00.19842021-03-010132021False0MondayTrueWinterFalseNightno rain0
10.0-1.19832021-03-011132021False0MondayTrueWinterFalseNightno rain0
20.0-1.29842021-03-012132021False0MondayTrueWinterFalseNightno rain1
30.0-0.910052021-03-013132021False0MondayTrueWinterFalseNightno rain0
40.00.010062021-03-014132021False0MondayTrueWinterFalseNightno rain0
50.02.49862021-03-015132021False0MondayTrueWinterFalseNightno rain0
60.02.49862021-03-016132021False0MondayTrueWinterTrueNightno rain0
70.02.110042021-03-017132021False0MondayTrueWinterTrueMorningno rain3
80.05.19852021-03-018132021False0MondayTrueWinterTrueMorningno rain1
90.05.79852021-03-019132021False0MondayTrueWinterTrueMorningno rain4

Last rows

raintemprhumwdspdatehourdaymonthyearholidaydayofweek_ndayofweekworking_dayseasonpeaktimesofdayrain_typecount
87500.27.98392022-02-28142822022False0MondayTrueWinterFalseAfternoondrizzle0
87510.97.49082022-02-28152822022False0MondayTrueWinterTrueAfternoonmoderate rain0
87520.08.38142022-02-28162822022False0MondayTrueWinterTrueAfternoonno rain0
87530.08.07582022-02-28172822022False0MondayTrueWinterTrueAfternoonno rain0
87540.04.58192022-02-28182822022False0MondayTrueWinterTrueEveningno rain0
87550.02.58662022-02-28192822022False0MondayTrueWinterTrueEveningno rain0
87560.02.28672022-02-28202822022False0MondayTrueWinterFalseEveningno rain0
87570.01.19052022-02-28212822022False0MondayTrueWinterFalseEveningno rain0
87580.00.09462022-02-28222822022False0MondayTrueWinterFalseEveningno rain0
87590.00.29462022-02-28232822022False0MondayTrueWinterFalseNightno rain0